Identity-representation itself is a prominent topic in AI today [1]}, and like many other human traits its manifestation within AI can be limited. The warnings are quite realistic, as bias created from the developers themselves [2]}, or through pre-existing archival (i. e.  data from historic sources) data [3]}, data-driven machine learning algorithms will simply replicate this. In this regard, the need for multimodal, representative data is more prevalent than ever. Not only, due to the aforementioned demographic biasses, including for gender-based variables [4]}, which can occur during collection, but also for representation and usability on a global scale, improving the overall impact of intelligent HCIs. As well as this, for effective Human Computer Interactions (HCIs) social robotics require observation of a scene through multiple modalities by for example enhancing the ability for gesture recognition [5]}. In the age of deep learning, multimodal interactions are the next stage for enhancing the usability of such algorithms [6]}, [7]}. Offering benefits across domains, including conditions with a broad variety in population needs, such as Autism [8]}.
